A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for...
Ausführliche Beschreibung
Autor*in: |
Rauch, Geraldine [verfasserIn] Kunzmann, Kevin - 1988- [verfasserIn] Kieser, Meinhard [verfasserIn] Wegscheider, Karl [verfasserIn] König, Jochem [verfasserIn] Eulenburg, Christine Helene - Gräfin zu, 1980- [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2018 |
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Schlagwörter: |
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Anmerkung: |
First published: 4 December 2017 Gesehen am 13.04.2018 |
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Umfang: |
19 |
Übergeordnetes Werk: |
Enthalten in: Statistics in medicine - Chichester [u.a.] : Wiley, 1982, 37(2018), 5, Seite 749-767 |
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Übergeordnetes Werk: |
volume:37 ; year:2018 ; number:5 ; pages:749-767 ; extent:19 |
Links: |
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DOI / URN: |
10.1002/sim.7531 |
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Katalog-ID: |
1572000678 |
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245 | 1 | 2 | |a A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance |c Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg |
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520 | |a Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. | ||
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10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
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10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
allfields_unstemmed |
10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
allfieldsGer |
10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
allfieldsSound |
10.1002/sim.7531 doi (DE-627)1572000678 (DE-576)502000678 (DE-599)BSZ502000678 (OCoLC)1341007097 DE-627 ger DE-627 rda eng Rauch, Geraldine verfasserin (DE-588)139823492 (DE-627)61378944X (DE-576)313422923 aut A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg 2018 19 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier First published: 4 December 2017 Gesehen am 13.04.2018 Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. clinical trials composite endpoint relevance weighting time‐to‐event Kunzmann, Kevin 1988- verfasserin (DE-588)1109931751 (DE-627)86460789X (DE-576)475679687 aut Kieser, Meinhard verfasserin (DE-588)103276547X (DE-627)739277766 (DE-576)168963825 aut Wegscheider, Karl verfasserin (DE-588)1027988687 (DE-627)73010060X (DE-576)375470107 aut König, Jochem verfasserin (DE-588)113711813X (DE-627)894065742 (DE-576)168752344 aut Eulenburg, Christine Helene Gräfin zu 1980- verfasserin (DE-588)139771034 (DE-627)613095448 (DE-576)313046859 aut Enthalten in Statistics in medicine Chichester [u.a.] : Wiley, 1982 37(2018), 5, Seite 749-767 Online-Ressource (DE-627)30246719X (DE-600)1491221-1 (DE-576)079719686 1097-0258 nnns volume:37 year:2018 number:5 pages:749-767 extent:19 http://dx.doi.org/10.1002/sim.7531 Verlag Resolving-System Volltext https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 Verlag Volltext GBV_USEFLAG_U GBV_ILN_2013 ISIL_DE-16-250 SYSFLAG_1 GBV_KXP SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_252 GBV_ILN_266 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2018 5 749-767 19 2013 01 DE-16-250 300603449X 00 --%%-- --%%-- --%%-- --%%-- l01 13-04-18 2013 01 DE-16-250 00 s hd2018 2013 01 DE-16-250 01 s (DE-627)1410508463 wissenschaftlicher Artikel (Zeitschrift) 2013 01 DE-16-250 02 s per_6 2013 01 DE-16-250 03 s s_19 2013 01 DE-16-250 04 p (DE-627)1448427592 Rauch, Geraldine 2013 01 DE-16-250 04 k (DE-627)1416466967 Medizinische Fakultät Heidelberg 2013 01 DE-16-250 04 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 04 s pos_1 2013 01 DE-16-250 05 p (DE-627)1545679916 Kunzmann, Kevin 2013 01 DE-16-250 05 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 05 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 05 s pos_ 2 2013 01 DE-16-250 06 p (DE-627)1450272401 Kieser, Meinhard 2013 01 DE-16-250 06 k (DE-627)1416741593 Institut für Medizinische Biometrie und Informatik 2013 01 DE-16-250 06 s (DE-627)1410501914 Verfasser 2013 01 DE-16-250 06 s pos_3 |
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Rauch, Geraldine @@aut@@ Kunzmann, Kevin @@aut@@ Kieser, Meinhard @@aut@@ Wegscheider, Karl @@aut@@ König, Jochem @@aut@@ Eulenburg, Christine Helene @@aut@@ |
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A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance Geraldine Rauch, Kevin Kunzmann, Meinhard Kieser, Karl Wegscheider, Jochem König, Christine Eulenburg |
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A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance |
abstract |
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018 |
abstractGer |
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018 |
abstract_unstemmed |
Composite endpoints combine several events within a single variable, which increases the number of expected events and is thereby meant to increase the power. However, the interpretation of results can be difficult as the observed effect for the composite does not necessarily reflect the effects for the components, which may be of different magnitude or even point in adverse directions. Moreover, in clinical applications, the event types are often of different clinical relevance, which also complicates the interpretation of the composite effect. The common effect measure for composite endpoints is the all?cause hazard ratio, which gives equal weight to all events irrespective of their type and clinical relevance. Thereby, the all?cause hazard within each group is given by the sum of the cause?specific hazards corresponding to the individual components. A natural extension of the standard all?cause hazard ratio can be defined by a ?weighted all?cause hazard ratio? where the individual hazards for each component are multiplied with predefined relevance weighting factors. For the special case of equal weights across the components, the weighted all?cause hazard ratio then corresponds to the standard all?cause hazard ratio. To identify the cause?specific hazard of the individual components, any parametric survival model might be applied. The new weighted effect measure can be tested for deviations from the null hypothesis by means of a permutation test. In this work, we systematically compare the new weighted approach to the standard all?cause hazard ratio by theoretical considerations, Monte?Carlo simulations, and by means of a real clinical trial example. First published: 4 December 2017 Gesehen am 13.04.2018 |
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container_issue |
5 |
title_short |
A weighted combined effect measure for the analysis of a composite time‐to‐first‐event endpoint with components of different clinical relevance |
url |
http://dx.doi.org/10.1002/sim.7531 https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7531 |
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Rauch, G. Rauch, Geraldine Kunzmann, Kevin Kieser, M. Kieser, Meinhard Wegscheider, Karl Koenig, Jochem König, Jochem Kleingarn, Christine Helene Eulenburg, Christine Eulenburg, Christine zu Eulenburg, Christine Helene zu |
GND_txt_mv |
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GND_txtF_mv |
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doi_str |
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up_date |
2024-07-04T20:40:20.377Z |
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